Implementation of a Prospective Index-Cluster Sampling Strategy for the Detection of Presymptomatic Viral Respiratory Infection in Undergraduate Students.

Background: Index-cluster studies may help characterize the spread of communicable infections in the presymptomatic state. We describe a prospective index-cluster sampling strategy (ICSS) to detect presymptomatic respiratory viral illness and its implementation in a college population. Methods: We enrolled an annual cohort of first-year undergraduates who completed daily electronic symptom diaries to identify index cases (ICs) with respiratory illness. Investigators then selected 5-10 potentially exposed, asymptomatic close contacts (CCs) who were geographically co-located to follow for infections. Symptoms and nasopharyngeal samples were collected for 5 days. Logistic regression model-based predictions for proportions of self-reported illness were compared graphically for the whole cohort sampling group and the CC group. Results: We enrolled 1379 participants between 2009 and 2015, including 288 ICs and 882 CCs. The median number of CCs per IC was 6 (interquartile range, 3-8). Among the 882 CCs, 111 (13%) developed acute respiratory illnesses. Viral etiology testing in 246 ICs (85%) and 719 CCs (82%) identified a pathogen in 57% of ICs and 15% of CCs. Among those with detectable virus, rhinovirus was the most common (IC: 18%; CC: 6%) followed by coxsackievirus/echovirus (IC: 11%; CC: 4%). Among 106 CCs with a detected virus, only 18% had the same virus as their associated IC. Graphically, CCs did not have a higher frequency of self-reported illness relative to the whole cohort sampling group. Conclusions: Establishing clusters by geographic proximity did not enrich for cases of viral transmission, suggesting that ICSS may be a less effective strategy to detect spread of respiratory infection.

Discriminative value of inflammatory biomarkers for suspected sepsis.

BACKGROUND: Circulating biomarkers can facilitate sepsis diagnosis, enabling early management and improved outcomes. Procalcitonin (PCT) has been suggested to have superior diagnostic utility compared to other biomarkers. STUDY OBJECTIVES: To define the discriminative value of PCT, interleukin-6 (IL-6), and C-reactive protein (CRP) for suspected sepsis. METHODS: PCT, CRP, and IL-6 were correlated with infection likelihood, sepsis severity, and septicemia. Multivariable models were constructed for length-of-stay and discharge to a higher level of care. RESULTS: Of 336 enrolled subjects, 60% had definite infection, 13% possible infection, and 27% no infection. Of those with infection, 202 presented with sepsis, 28 with severe sepsis, and 17 with septic shock. Overall, 21% of subjects were septicemic. PCT, IL6, and CRP levels were higher in septicemia (median PCT 2.3 vs. 0.2 ng/mL; IL-6 178 vs. 72 pg/mL; CRP 106 vs. 62 mg/dL; p < 0.001). Biomarker concentrations increased with likelihood of infection and sepsis severity. Using receiver operating characteristic analysis, PCT best predicted septicemia (0.78 vs. IL-6 0.70 and CRP 0.67), but CRP better identified clinical infection (0.75 vs. PCT 0.71 and IL-6 0.69). A PCT cutoff of 0.5 ng/mL had 72.6% sensitivity and 69.5% specificity for bacteremia, as well as 40.7% sensitivity and 87.2% specificity for diagnosing infection. A combined clinical-biomarker model revealed that CRP was marginally associated with length of stay (p = 0.015), but no biomarker independently predicted discharge to a higher level of care. CONCLUSIONS: In adult emergency department patients with suspected sepsis, PCT, IL-6, and CRP highly correlate with several infection parameters, but are inadequately discriminating to be used independently as diagnostic tools.

A blood-based host gene expression assay for early detection of respiratory viral infection: an index-cluster prospective cohort study.

BACKGROUND: Early and accurate identification of individuals with viral infections is crucial for clinical management and public health interventions. We aimed to assess the ability of transcriptomic biomarkers to identify naturally acquired respiratory viral infection before typical symptoms are present. METHODS: In this index-cluster study, we prospectively recruited a cohort of undergraduate students (aged 18-25 years) at Duke University (Durham, NC, USA) over a period of 5 academic years. To identify index cases, we monitored students for the entire academic year, for the presence and severity of eight symptoms of respiratory tract infection using a daily web-based survey, with symptoms rated on a scale of 0-4. Index cases were defined as individuals who reported a 6-point increase in cumulative daily symptom score. Suspected index cases were visited by study staff to confirm the presence of reported symptoms of illness and to collect biospecimen samples. We then identified clusters of close contacts of index cases (ie, individuals who lived in close proximity to index cases, close friends, and partners) who were presumed to be at increased risk of developing symptomatic respiratory tract infection while under observation. We monitored each close contact for 5 days for symptoms and viral shedding and measured transcriptomic responses at each timepoint each day using a blood-based 36-gene RT-PCR assay. FINDINGS: Between Sept 1, 2009, and April 10, 2015, we enrolled 1465 participants. Of 264 index cases with respiratory tract infection symptoms, 150 (57%) had a viral cause confirmed by RT-PCR. Of their 555 close contacts, 106 (19%) developed symptomatic respiratory tract infection with a proven viral cause during the observation window, of whom 60 (57%) had the same virus as their associated index case. Nine viruses were detected in total. The transcriptomic assay accurately predicted viral infection at the time of maximum symptom severity (mean area under the receiver operating characteristic curve [AUROC] 0·94 [95% CI 0·92-0·96]), as well as at 1 day (0·87 [95% CI 0·84-0·90]), 2 days (0·85 [0·82-0·88]), and 3 days (0·74 [0·71-0·77]) before peak illness, when symptoms were minimal or absent and 22 (62%) of 35 individuals, 25 (69%) of 36 individuals, and 24 (82%) of 29 individuals, respectively, had no detectable viral shedding. INTERPRETATION: Transcriptional biomarkers accurately predict and diagnose infection across diverse viral causes and stages of disease and thus might prove useful for guiding the administration of early effective therapy, quarantine decisions, and other clinical and public health interventions in the setting of endemic and pandemic infectious diseases. FUNDING: US Defense Advanced Research Projects Agency.

An Infection Control Program for a 2009 influenza A H1N1 outbreak in a university-based summer camp.

OBJECTIVES: Describe two 2009-H1N1 influenza outbreaks in university-based summer camps and the implementation of an infection control program. PARTICIPANTS: 7,906 campers across 73 residential camps from May 21-August 2, 2009. METHODS: Influenza-like-illness (ILI) was defined as fever with cough and/or sore throat. Influenza A was identified using PCR or rapid-antigen testing. We implemented an infection control program consisting of education, hand hygiene, disinfection, symptom screening, and ILI case management. RESULTS: An initial ILI cluster involved 60 cases across 3 camps from June 17-July 2. Academic Camp-1 had the most cases (n = 45, 14.9% attack rate); influenza A was identified in 84% of those tested. Despite implementation of an infection control program, a second ILI cluster began on July 12 in Academic Camp-2 (n = 47, 15.0% attack rate). CONCLUSIONS: ILI can spread rapidly in a university-based residential camp. Infection control is an important aspect of the medical response but is challenging to implement.